Scalable Monitoring Heuristics for Improving Network Latency - LAAS - Laboratoire d'Analyse et d'Architecture des Systèmes Access content directly
Conference Papers Year : 2020

Scalable Monitoring Heuristics for Improving Network Latency


We consider a routing overlay in which the delay of a path can be obtained at some fixed cost by sending probe packets, and investigate the joint minimization of the probing cost and the routing delay. Assuming that link delays are modelled by Markov chains, this problem can be cast as a Markov Decision Process (MDP). Unfortunately, computing the exact solution of this MDP is prohibitively expensive due to the well-known "curse of dimensionality". In this work we propose two scalable approaches that are fast enough to provide efficient solutions on practical time scales. We analyze the complexity of both approaches, and evaluate their accuracy in small synthetic scenarios for which the optimal monitoring policy can be computed. Finally, the robustness and the scalability of the proposed solutions are analyzed using real delay data collected over the Internet.
Fichier principal
Vignette du fichier
report.pdf (614.07 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-02413636 , version 1 (16-12-2019)



Maxime Mouchet, Martin Randall, Marine Ségneré, Isabel Amigo, Pablo Belzarena, et al.. Scalable Monitoring Heuristics for Improving Network Latency. NOMS 2020 : IEEE/IFIP Network Operations and Management Symposium, Apr 2020, Budapest, Hungary. ⟨10.1109/NOMS47738.2020.9110341⟩. ⟨hal-02413636⟩
124 View
83 Download



Gmail Mastodon Facebook X LinkedIn More